Function generate_logistic_model

tests/python/generate_models.py:69–104

()

def generate_logistic_model() -> None:
    print("Logistic")
    X, y = make_classification(n_samples=kRows, n_features=kCols, random_state=2025)
    assert y.max() == 1 and y.min() == 0
    w = np.random.default_rng(2025).uniform(size=X.shape[0])

    for objective, name in [
        ("binary:logistic", "logit"),
        ("binary:logitraw", "logitraw"),
    ]:
        data = xgboost.DMatrix(X, label=y, weight=w)
        booster = xgboost.train(
            {
                "tree_method": "hist",
                "num_parallel_tree": kForests,
                "max_depth": kMaxDepth,
                "objective": objective,
                "base_score": 0.5,
            },
            num_boost_round=kRounds,
            dtrain=data,
        )
        booster.save_model(booster_ubj(name))
        booster.save_model(booster_json(name))

        reg = xgboost.XGBClassifier(
            tree_method="hist",
            num_parallel_tree=kForests,
            max_depth=kMaxDepth,
            n_estimators=kRounds,
            objective=objective,
            base_score=0.5,
        )
        reg.fit(X, y, sample_weight=w)
        reg.save_model(skl_ubj(name))
        reg.save_model(skl_json(name))


def generate_classification_model() -> None: